Udit Mehrotra created SPARK-20515:
-------------------------------------
Summary: Issue with reading Hive ORC tables having char/varchar
columns in Spark SQL
Key: SPARK-20515
URL: https://issues.apache.org/jira/browse/SPARK-20515
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.0.2
Environment: AWS EMR Cluster
Reporter: Udit Mehrotra
Reading from a Hive ORC table containing char/varchar columns fails in Spark
SQL. This is caused by the fact that Spark SQL internally replaces the
char/varchar columns with String data type. So, while reading from the table
created in Hive which has varchar/char columns, it ends up using the wrong
reader and causes a ClassCastException.
Here is the exception:
java.lang.ClassCastException:
org.apache.hadoop.hive.serde2.io.HiveVarcharWritable cannot be cast to
org.apache.hadoop.io.Text
at
org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableStringObjectInspector.getPrimitiveWritableObject(WritableStringObjectInspector.java:41)
at
org.apache.spark.sql.hive.HiveInspectors$class.unwrap(HiveInspectors.scala:324)
at
org.apache.spark.sql.hive.HadoopTableReader$.unwrap(TableReader.scala:333)
at
org.apache.spark.sql.hive.HadoopTableReader$$anonfun$14$$anonfun$apply$15.apply(TableReader.scala:419)
at
org.apache.spark.sql.hive.HadoopTableReader$$anonfun$14$$anonfun$apply$15.apply(TableReader.scala:419)
at
org.apache.spark.sql.hive.HadoopTableReader$$anonfun$fillObject$2.apply(TableReader.scala:435)
at
org.apache.spark.sql.hive.HadoopTableReader$$anonfun$fillObject$2.apply(TableReader.scala:426)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:247)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at
org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at
org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
at org.apache.spark.scheduler.Task.run(Task.scala:86)
at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
While the issue has been fixed in Spark 2.1.1 and 2.2.0 with SPARK-19459, it
still needs to be fixed Spark 2.0.
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